PREDICTING KEYWORD MONETIZATION

• US 20090299855A1
• Filed: 06/02/2008
• Published: 12/03/2009
• Est. Priority Date: 06/02/2008
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Li, Hua, Chen, Zheng, Wang, Jian
• Granted Patent: US 8,682,839 B2
• Time in Patent Office: Days
• US Class Current: 705/14.54
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0256 User search
• CPC Class Codes: G06Q 30/0267 Wireless devices
• CPC Class Codes: G06Q 30/08 Auctions

Abstract

Embodiments of the claimed subject matter provide a method and system for predicting bidding keyword monetization. The claimed subject matter provides a method and system with which the value of a keyword for the purpose of relevant online advertisement may be evaluated according to various metrics to determine a bidding landscape for use in advertising campaigns. The value of the keyword considers certain attributes related to the monetization of the keyword.

One embodiment of the claimed subject matter is implemented as a method for predicting keyword monetization for one or more keyword-advertisement relationships. Historical data for the one or more keyword-advertisement relationships is referenced and used to generate a global model of the one or more keyword-advertisement relationship. The relationships are then evaluated according to a time-series analysis, which parses the data from the historical data and the global model to create predictions for the keyword monetization according to the keyword-advertisement relationships.

Claims

1. A method of predicting keyword monetization for a period of time for one or more advertisements corresponding to a keyword in one or more keyword-advertiser relationships, the method comprising:

fetching historical data for the one or more keyword-advertiser relationships; and

predicting keyword monetization based on historical data for the one or more keyword-advertiser relationships.

2. The method of claim 1, wherein predicting keyword monetization further comprises:

generating a global model incorporating the one or more keyword-advertiser relationships from the historical data of the one or more keyword-advertiser relationships; and

evaluating the one or more keyword-advertiser relationships according to a time-series analysis.

3. The method of claim 2, wherein generating a global model incorporating a keyword-advertiser relationship further comprises:

calculating an average cost per click rate for the keyword from the historical data of the keyword-advertiser relationship; and

calculating an average clickthrough rate for the keyword from the historical data of the keyword-advertiser relationship.

4. The method of claim 2, wherein evaluating a keyword-advertiser relationship according to a time series analysis further comprises:

predicting a number of impressions for an advertisement from the keyword-advertiser relationship that will be displayed in the period of time to be predicted;

predicting a cost for an advertiser to advertise with the keyword in the period of time to be predicted;

predicting a number of clicks generated by an advertisement from the keyword-advertiser relationship in the period of time to be predicted;

predicting a bidding price for the keyword for the period of time to be predicted; and

predicting a number of conversions generated by an advertisement from the keyword-advertiser relationship in the period of time to be predicted.

5. The method of claim 2, wherein evaluating a keyword-advertiser relationship according to a time series analysis further comprises predicting an average position for an advertisement from the keyword-advertiser relationship displayed in a search return list generated by a search engine in response to a search query which includes the keyword during the period of time to be predicted.

6. The method of claim 5, wherein predicting an average position for an advertisement from the keyword-advertiser relationship during the period of time to be predicted further comprises:

calculating a historical average position for an advertisement from the keyword-advertiser relationship displayed in a search return list generated by a search engine in response to a search query which includes the keyword; and

applying one or more regression models to the historical average position of the keyword.

7. The method of claim 6, wherein the one or more regression models comprises:

a linear regression model;

a least-square regression model; and

a slope-based regression model.

8. The method of claim 5 wherein predicting an average position for an advertisement from the keyword-advertiser relationship during the period of time to be predicted further comprises finding a corresponding period of time to be predicted from the historical data of the keyword-advertiser relationship.

9. The method of claim 4, wherein predicting a number of impressions for an advertisement from the keyword-advertiser relationship further comprises applying constraints from the generated global model.

10. The method of claim 4, wherein predicting a number of conversions generated by an advertisement from the keyword-advertiser relationship in the period of time to be predicted further comprises calculating an average value of the keyword-advertiser performance from the historical data.

11. A method for generating a keyword performance landscape for one or more keyword-advertiser relationships representing one or more advertisers with one or more advertisements corresponding to a keyword, the method comprising:

connecting to a data resource containing data for the one or more keyword-advertiser relationships, the data including historical data of the one or more keyword-advertiser relationships;

fetching the historical data for the one or more keyword-advertiser relationships, the historical data including keyword monetization property for the one or more keyword-advertiser relationships;

predicting keyword monetization property for the keyword for a period of time, predicting keyword monetization includes generating a global model incorporating the one or more keyword-advertiser relationships and evaluating the one or more keyword-advertiser relationships according to a time-series analysis; and

generating a keyword performance landscape for the one or more keyword-advertiser relationship from the historical data and the predicted keyword monetization property.

12. The method of claim 11, wherein keyword monetization property for a keyword-advertiser relationship comprises:

an average bidding price for an advertisement corresponding to the keyword;

a number of impressions of advertisements for an advertisement from the keyword-advertiser relationship displayed in the period of time;

a number of clicks generated by an advertisement from the keyword-advertiser relationship during the period of time;

a cost incurred by the advertiser to advertise with the keyword during the period of time; and

a number of conversions generated by advertisements of the keyword during the period of time.

13. The method of claim 12, wherein keyword monetization property for a keyword-advertiser relationship further comprises an average position for an advertisement from the keyword-advertiser relationship displayed in a search return list generated by a search engine in response to a search query which includes the keyword during the period of time.

14. The method of claim 11, wherein the period of time is a day.

15. The method of claim 11 further comprising outputting the keyword performance landscape.

16. A system for generating a bidding keyword landscape including one or more keyword-advertiser relationships, the system comprising:

a computer system having a processor coupled to a memory, the memory having computer readable code, which when executed by the processor causes the computer system to implement;

a advertiser production data resource; and

a keyword monetization engine wherein the keyword monetization engine predicts monetization for a keyword for a duration of time by generating a global model incorporating the one or more keyword-advertiser relationships and evaluating the one or more keyword-advertiser relationships according to a time-series analysis, generating a keyword performance landscape from the global model and time series analysis, and outputting the keyword performance landscape.

17. The system according to claim 16, wherein the advertisements are displayed on a remote computing device.

18. The system according to claim 17, wherein the remote computing device is a computer system.

19. The system according to claim 17, wherein the remote computing device is a hand-held computing device.

20. The system according to claim 17, wherein the advertisements are displayed in a search result list generated by a search engine that is accessed by a user over the Internet.